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Dataset results
6 results for “sentence level”
Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval
<p>This submission includes all pretrained models, test data and prediction files for the EMNLP 2019 paper "Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval". Please follow the instructions in the emnlp bran at the <a href="https://github.com/castorini/birch/tree/emnlp">Birch repo</a> to reproduce the results.</p>
SimPA: A Sentence-Level Simplification Corpus for the Public Administration Domain
<p>We present a sentence-level simplification corpus with content from the Public Administration (PA) domain. The corpus contains 1,100 original sentences with manual simplifications collected through a two-stage process. Firstly, annotators were asked to simplify only words and phrases (lexical simplification). Each sentence was simplified by three annotators. Secondly, one lexically simplified version of each original sentence was further simplified at the syntactic level. In its current version there are 3,300 lexically simplified sentences plus 1,100 syntactically simplified sentences. The corpus will be used for evaluation of text simplification approaches in the scope of the EU H2020 SIMPATICO project - which focuses on accessibility of e-services in the PA domain - and beyond. The main advantage of this corpus is that lexical and syntactic simplifications can be analysed and used in isolation. The lexically simplified corpus is also multi-reference (three different simplifications per original sentence). This is an ongoing effort and our final aim is to collect manual simplifications for the entire set of original sentences, with over 10K sentences.</p>
KEANE (faKe nEws At seNtence lEvel) dataset.
<p>This dataset is aimed at developing systems for detecting fake news. It contains links to articles, posts and other types of publications that have been previously evaluated by verifying entities. Sentence-level check-worthiness and fact-checking annotations are provided for each of these news items.</p>
NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization
<p>Accessing medical literature is difficult for laypeople as the content is written for specialists and contains medical jargon. Automated text simplification methods offer a potential means to address this issue. In this work, we propose a summarize-then-simplify two-stage strategy, which we call NapSS, identifying the relevant content to simplify while ensuring that the original narrative flow is preserved. In this approach, we first generate reference summaries via sentence matching between the original and the simplified abstracts. These summaries are then used to train an extractive summarizer, learning the most relevant content to be simplified. Then, to ensure the narrative consistency of the simplified text, we synthesize auxiliary narrative prompts combining key phrases derived from the syntactical analyses of the original text. Our model achieves results significantly better than the seq2seq baseline on an English medical corpus, yielding 3%~4% absolute improvements in terms of lexical similarity, and providing a further 1.1% improvement of SARI score when combined with the baseline. We also highlight shortcomings of existing evaluation methods, and introduce new metrics that take into account both lexical and high-level semantic similarity. A human evaluation conducted on a random sample of the test set further establishes the effectiveness of the proposed approach.</p>
A dataset for sentence-level complexity in Russian
<p>A novel dataset with sentence-level annotation of complexity. The dataset is open and contains 1,200 Russian sentences extracted from SynTagRus treebank. Annotations were collected via Yandex Toloka platform using 7-point scale.</p>
Data for: Benchmarking the Sentence-Level Simplification of Dutch Municipal Text
<p><a href="https://github.com/Amsterdam-AI-Team/dutch-municipal-text-simplification/blob/master/complex-simple-sentences/complex-simple-v1-anonymized.csv">The corpus</a> consists of 1311 automatically aligned complex-simple sentence pairs.</p> <div> <h2>Original documents</h2> <a href="https://github.com/Amsterdam-AI-Team/dutch-municipal-text-simplification/tree/master/complex-simple-sentences#original-documents"></a></div> <p>For the creation of the dataset, we have used ~50 documents provided by the Communications Department of the City of Amsterdam. The documents have diverse sources and purposes (e.g. reports, citizen letters, newsletters, etc.) and cover a variety of topics (legal, medical, urban planning, etc.).</p> <p>The documents were reviewed by an expert and contain edits related to simplification, but also tone of voice, spelling corrections and other improvements. For the alignment of the sentence, we have used the original and final version of the documents.</p> <div> <h2>Alignment</h2> <a href="https://github.com/Amsterdam-AI-Team/dutch-municipal-text-simplification/tree/master/complex-simple-sentences#alignment"></a></div> <p>The alignment processed consists of 2 steps. For each document, create candidate complex-simple pairs by:</p> <ol> <li>aligning paragraphs (based on TF-IDF similarity)</li> <li>aligning sentences within the aligned paragraphs (based on TF-IDF similarity)</li> </ol> <div> <h2>Post-processeding</h2> <a href="https://github.com/Amsterdam-AI-Team/dutch-municipal-text-simplification/tree/master/complex-simple-sentences#post-processeding"></a></div> <p>After the initial alignment of sentences we:</p> <ul> <li>filter the candidate pairs where differences are only in capitalization or punctuation</li> <li>drop duplicates</li> <li>for every complex sentence where there are multiple possible simple versions, create a new entry by merging all simple versions (under the assumption that a complex sentence was simplified by splitting it into 2 or more simple sentences)</li> <li>for every complex sentence, preserve the simple version with the lowest Levenshtein edit-distance</li> </ul> <div> <h2>Anonymization</h2> <a href="https://github.com/Amsterdam-AI-Team/dutch-municipal-text-simplification/tree/master/complex-simple-sentences#anonymization"></a></div> <p>In order to publish the dataset, the following changes have been performed to the dataset:</p> <p>We have removed:</p> <ul> <li>names (including those of people with public functions such as the gemeentesecretaris) -> substituted with [NAME]</li> <li>organizations (with the exception of public organization such as Gemeente Amsterdam and GGD, RIVM) -> substituted with [ORGANIZATION]</li> <li>addresses (whenever they included an exact street and number) -> substituted with [ADDRESS]</li> <li>phone numbers -> substituted with [NUMBER]</li> <li>a handful of sentence posing privacy or information security risks, or containing otherwise sensitive information -> replaced by "xxx xxx xxx" for transparency</li> </ul> <p>Finally, the anonymized version of the dataset does not contain further information about the source documents (e.g. their names), however, a document ID has been added in order to provide context information about sentences stemming from the same document.</p> <div> <h2>Acknowledgements</h2> <a href="https://github.com/Amsterdam-AI-Team/dutch-municipal-text-simplification/tree/master/complex-simple-sentences#acknowledgements"></a></div> <p>This dataset was created by <a href="https://amsterdamintelligence.com/" rel="nofollow">Amsterdam Intelligence</a> for the City of Amsterdam.</p> <p>We owe a special thank you to the Communications Department of the City of Amsterdam for providing us with the original 48 documents. We also thank <a href="https://www.linkedin.com/in/daniel-v-a60905139/" rel="nofollow">Daniel Vlantis</a> for providing feedback during the dataset creation and for extensive experiments with it.</p> <div> <h2>License</h2> <a href="https://github.com/Amsterdam-AI-Team/dutch-municipal-text-simplification/tree/master/complex-simple-sentences#license"></a></div> <p>This data is licensed under the terms of the European Union Public License 1.2 (EUPL-1.2).</p>
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